Efficient Nonlinear RX Anomaly Detectors
This work provides more efficient anomaly detection methods for practitioners working with multi- and hyperspectral images, addressing the bottleneck of slow nonlinear detectors.
This paper addresses the trade-off between accuracy and efficiency in anomaly detection by proposing two methods to accelerate the kernel Reed-Xiaoli (RX) algorithm. They achieve similar or improved performance compared to the standard kernel RX while significantly reducing computational cost, with the Nyström approach showing improved detection power.
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nyström approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks to their implicit regularization effect. Last but not least, the Nyström approach has an improved power of detection.